{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "638f392b",
   "metadata": {},
   "source": [
    "# 在分类时间序列中查找周期性"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a65bb62",
   "metadata": {},
   "source": [
    "说明如何对分类值时间序列数据执行频谱分析。当您对值本质上不是数值的数据的循环行为感兴趣时，分类值时间序列的谱分析很有用。\n",
    "这些数据来自对新生儿睡眠状态的研究。一位儿科神经科医生每分钟对婴儿的脑电图 (EEG) 记录进行评分，持续大约两个小时。神经科医生将婴儿的睡眠状态分类为以下之一：\n",
    "\n",
    "qt- 安静的睡眠，痕迹交替\n",
    "qh- 安静的睡眠，高压\n",
    "tr- 过渡睡眠\n",
    "al- 主动睡眠，低电压\n",
    "ah- 主动睡眠，高压\n",
    "aw- 苏醒"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "808066e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sleep State')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "data= np.array(['ah','ah','ah','ah','ah','ah','ah','ah','tr','ah','tr','ah',\n",
    "   'ah','qh','qt','qt','qt','qt','qt','tr','qt','qt','qt','qt','qt',\n",
    "   'qt','qt','qt','qt','qt','tr','al','al','al','al','al','tr','ah',\n",
    "   'al','al','al','al','al','ah','ah','ah','ah','ah','ah','ah','tr',\n",
    "   'tr','ah','ah','ah','ah','tr','tr','tr','qh','qh','qt','qt','qt',\n",
    "   'qt','qt','qt','qt','qt','qt','qt','qt','qt','qt','qt','qt','qt',\n",
    "   'qt','qt','tr','al','al','al','al','al','al','al','al','al','al',\n",
    "   'al','al','al','al','al','al','al','ah','ah','ah','ah','ah','ah',\n",
    "   'ah','ah','ah','tr'])\n",
    "# 特征编码\n",
    "replace_num ={'qt':'1.0','qh':'2.0','tr':'3.0','al':'4.0','ah':'5.0','aw':'6.0'}\n",
    "datasix=[replace_num[i] if i in replace_num else i for i in data]\n",
    "datasix=np.array(datasix,dtype=np.float32)\n",
    "plt.plot(datasix)\n",
    "plt.grid()\n",
    "plt.xlabel('Minutes')\n",
    "plt.ylabel('Sleep State')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd79407",
   "metadata": {},
   "source": [
    "当关注最安静状态（1 和 2）和最活跃状态（4 和 5）之间的转换时，数据会表现出循环行为。要确定该行为的周期，使用频谱分析。睡眠状态是以一分钟的间隔分配的。以一分钟为间隔对数据进行采样相当于每小时对数据进行 60 次采样。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "131da330",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Periodogram of Sleep States')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import signal\n",
    "Fs = 60\n",
    "[F,Pxx] = signal.periodogram(signal.detrend(datasix),Fs,nfft=256)\n",
    "plt.plot(F,Pxx)\n",
    "plt.grid()\n",
    "plt.xlabel('Cycles/Hour')\n",
    "plt.title('Periodogram of Sleep States')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57ec61d4",
   "metadata": {},
   "source": [
    "光谱分析显示了一个清晰的峰值，表明数据中的主要振荡或循环。确定峰值的频率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "30e7cfc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.171875\n"
     ]
    }
   ],
   "source": [
    "# 找出最大值所对应的频率\n",
    "Pxx=Pxx.tolist()\n",
    "print(F[Pxx.index(max(Pxx))])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "360e6284",
   "metadata": {},
   "source": [
    "不是将睡眠状态分配为值 1 到 6，而是重复分析，仅关注安静睡眠和活动睡眠之间的区别。分配安静状态qt和qh，值 1。分配过渡状态，tr，值 2。最后，分配两个活动睡眠状态，al和ah，值 3。为了完整性，分配唤醒状态，aw值 4，甚至尽管状态不会出现在数据中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39fae263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sleep State')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4teJxGc80RkDFq48CAFFxdZHr7/dEERRl7Hro21R4l3kbkBiBnqzcRwDgSSJaB+A6AJ8noocR1B0S8jONEdjF+xEAvkGggkYlD6pnspCf0aT+NqNN0bMIFkl9Kt1IM/vOAbAPwLsAfAPATgCvrXJQXaC0GEFwAWnKvzwURVCYsetOyzObDhHJ4kFD0lw93sfMHjM7zHw5M18Mvyy1UAC3pBITSmoPGso4kR91cbqkCABJMNAR2VncEG5JMQIVLG5OEdhiCAoydr1ONK5XSFkS/ci6s5gArIPsLC6MU1KMQCmCZl1D8qMuQhcVgSwe9EJ2FjdE2SUmmtqVOhTXUGG6pwhkzujGstk3AXA/M5/HzPfC31X8nwBsqWNgpqMUgVXYNdSsIhj0LIxdD37vIiEPY6d7ikBiBHqxbPZ9A8BmACCiEwD8EMBxAN5GRB+sfmhm45XYvD78f90MexaYZ4ZNyM7IcTtRXkIx7NtiCDRjmSF4GjPfFdx+E4ArmPnt8APFf1z5yAyn7KJzTbmGpONUccaO14mCc4qhbUmwWDOWzb7wEu9MBI1pmHkMQH71BXE9hm1R4U1gjRsCW3aKFmXUMUMgu9H1Y9ns20FEFxHRuwCcAOBbAEBEG9J8MBEdRUTbiOjnRHQbEb0j5jVERBcT0d1EtIOITs5zEG3EZS6cOgr4iqJnUXNZQ31VTVJWeHkZO14nCs4pJMFAP5bNvr8A8Cj8OMEfMfO+4PETAVyU4rMdAH/DzCfCL1r3NiI6MfKaVwN4TvDvfACXpB96u1GKoAwGPUsUQYsRRSA0zbJ+BPsBLASFmfkHAH6w6oOZ+UEADwa3dxPR7QCOBPDz0MvOAfBZ9lNOrieiDUR0RPDe1nDXQ7txze0P4y+3HD/3+Ldu+w08Bs5+wbMW3uO4XFqRuGHPalARFI8RMDM+cs1deMNLjsKRGw4qa2ilctn37sGNv/Kzpi2L8PYzT8DznnVIps/Y9ouHceWP71t4/JE9o24Fi0URZOa3Byb42y/egv/8smPwyt85vPTPT1N0rjBEtBnASQBuiDx1JIDwL2NX8NicISCi8+ErBmzcuBHbt2/PNY49e/bkfu8y/u9dY3xt5wQneL9GP3Rxv+iG/fAYWPPo4sXt3vtG8DynlPG87JnAM51Hpp9V1XHGcedDDgDgB9f/CA8cmu9i9sQBDxdv34/HHrgXZx3TT/WeOo8RAP752/vgMmPDkHD/HkZ/36P4kxMGmT7jYz85gFsecbHx4PkFwDPXAE8fP4Tt2x9beE/dx1kHjz0ywm/3uo3M16YoeoyPH/Dwzdv24wg8AX4g3W8kC5UbgqBy6ZcBvJOZf5vnM5j5UgCXAsCpp57KW7ZsyTWW7du3I+97l3H9/juAnTtx2h+cjvVrZifpI7d9H57H2LLl9IX3XPPkz7Dmsd+UMp7oR1R1nHHQnY8AP/kRfu/FJ+GUY56e6zN+/dg+YPs2HHPscdjyiuNXvwH1HqP/B7+JPz15E/7xdb+LE/7+63j2pqOxZcvzMn3E5ff8CM/vjfH/3r44HxL/bN3HWQPXPnkrbn3igelxmXiMUYoe472P7QW2b8fvnfh8bDllU3kDC0jtTyCiQ4hofZYPJ6I+fCPweWb+SsxL7gdwVOj+puCxVqH8nVG5O5q4iUHUMmMETaJiBEWk/tj1vyOd/cajUEA3b8XVsdutoHASEiPIzrSdaUU70NO0qnwJEf0MwA4AtxLRLUR0Sor3EYDLANzOzB9OeNlWAG8MsodOA/BU2+IDQPKFbOx6iRO+zBhBk5TRaERNcl0vDsyMsevNpermqafftf0CSeT9/rqM+m1UtQM9jWvoMgB/xczfBQAiOh3ApwG8cMX7Xg7gvwD4GRH9NHjs7wEcDQDM/HEAXwfwGgB3w+958JaM49cC1WpwURF48BJKL7jMRnSkKqP14ChBUenCxGUwh8t52NNznoWR42HtsJawnNYMexYmLsPzzPgN1MFMEVSTVJBmVrrKCAAAM3+PiJxVb2Lm78GvVrrsNQzgbSnGoDVqdROnCLyE0guuZ4YiGJagCJJca7qgzm8ZikBcQyEV6XpYY3UnW6oIysXcpCL4DhF9AsAV8HcbvwHAdrX5i5lvrmRkLWKmCNzI4y6SSvA4hsQIVNqjyYpgNJn/EfrNeLJvoPP3C8iFbzpnJh7WdKQzW1GmrqGKFhJpDMGLgv8vjDx+EnzDcGapI2ohyxVB/Htcl9Gz2r86LCNGMNY8RqDOr5LluYPFoggAhOpTuS6A8lMhTWTqGmrKEDDzGZX8ZYNQq8PwipaZMXI8MCPWF2pejCB/iYnZ96dnmQql+OYVQZ4YgSvBYoTmTI44S1cZV2wI0mQNbSSiy4jo34P7JxLRWysZTUuJW9E6nh9gBBDrTzYlRtAtRTBLH81nCEQRAKG4kmQOpWamCKpxpaWZlZ8B8E0Azw7u3wngnZWMpqXEBTvDF7W4CW9OjKBEQ6DphSGaujfo5aunL+mjPmXMma5RdYwgzacexsxXIig9zcwOAD01fEPMgp3uwmNAvAT2DDEEPduCRSUFizV1FajzGo4RZD1e5SrsUk2hJKSHRXZUX/DGXEMA9hLRMxD0J1AbvyoZTUuJc22sVgSeEYYAKL5BSHdFMFpQBNb0h5mWiev7CcU1BAxs3xiKIkjPqGJFkCZr6N3wdwAfT0TfB3A4gHMrGU1LiUt/nFMHk8WLhusx+ob0qfU3WBkcLI5s78+jCKrOA28Ts4q1ep5vHWk8fZSZbyaiVwJ4LvwNYr9g5kklo2kpcSUS0sQI1vRFEQAtCBZHFEGe9NFxxJh0GelhkZ2R48Gi4j3Ok0iTNXQwgPfArx56K4DNRPTaSkbTUuLSR7sSIwCCFXIB/772G8oiqXsDO48iqLZWTJsoo4dF11C1roq2tk0izaz8NIAxgN8P7t8P4AOVjKalxK1oRykUgQnpo0CQV19AEehedG6Wwx0Ei/vZs4ZEEcwQRZCd0cStNNEgzaw8npn/CcAEAIKWlWZcwUpAZYMAsyqkQLKbSGFKGWogfxE2he6KIOqf9RVBNv/2TBFI1pD0uc5OuPptFaT55DERHYRZ1tDxAEaVjahlqGwQYN4FNJ9KujjhTdlHAJgfIxhFUveGPQseA06GY656Z2ibEEWQnao3I6bJGroQwDcAHEVEn4dfXvrNlY2oZYQvgHO3VygCP0ZgxkVhaGdPpwyTVKtJFxYUQWhnbC+lz1+pRdlQJvsI8jCqeDNimqyhq4noZgCnwXcJvYOZH61sRC0jnDY5rwjibytMihEM+xb2jFZWJk9EfYeqbLduNZiiweJwrZyDU7YtntYrEkNQSg+LrjGueDNioiFQZaZDqM5hRxPR0VJ+2ieNIoib8CbFCAZ2sdaD0e9Qtxr1Y5W6FyoxAWTbAKeC6eIaEtdQHppUBP97yXNSfjogOS6QwhBUlApWN8N+viJsiqiS0q1GfbRq6CCkCFJ/hiiCKZZFuVJwu8zYcTGsMPU40RBI+el0JMcF4jOIFI7HsG0zDEGpikDDi0NUls+qZ6aPi0wrmEqtIQDSwD4rI8fDugrbnCaamKBp/bNC999IRF8joouJ6OmVjahlpIkLxKePeubECHp2Kf0Iord1IZq6p24fyKQIqi0a1jaGObu8dZWx41W6GXHZJ38C/kYyENErAHwQwGfhF5y7tLIRtYzwqjDp4p+UPmoZ4hoqurpblWHVNKPJfOpennr6Y4kRzCGKIBsjx6t0M+IyrWEz8+PB7TcAuJSZvwzgy0T008pG1DKUIojWnxm7foDRIkpMHzVHERSMEQQ50iPH09JvPEpQBBIjyE/ROdM1mlQENhEpQ/EqAN8OPVeds6plqGyQ9Wv6C66hQc9KnPBGxQhKUATr1/Snt3XDVwThGEH2rCH1WjEEPqIIstFY+iiAKwB8h4geBbAfwHcBgIhOgPQjmKIm8yFregsB4mHPhm3FKwJTWlUC/o/a8Th3SuzY8XDoQX08umekZU+CaIwgT4etaAXTrlN0N3rXqLrf9bKsof9JRNcCOALAt5hVB15YAN5e2Yhahlrtr1/Tw2N7x6HH/RNnE8XGCFw2KH20N2s0ctAg+6pl5HhYv8afijp2KfMLfi0agizBzpHjwrYo9U5k0ymaYNA1xk2WmGDm62Meu7Oy0bQQtdJbt6aHB586MH18FPj04hSBFzS2N6XERLiBfR5DMHY8rAsMQZaUzLoYu/Ope4OcikACxTOKphx3jao3lMnMLIha1awfLsYIhn0/RhCVwI7ni6ueITGCPCtkhecxxq6H9UM/RqCjIogG6vLUyqn6h9w2im5C7BKux3A8brbWkLCcccg1FE2DVIogenFzA0NgUvookK92zNidfX/h+zoRTd0Lu8LSIopgHlEE6Yn2w6gCMQQFmcUI+gs7ZId9GzYtXtzcINxiSrC4SBGx8PcHtE0RZIkRiCIIM+zboghSUnW/YkBcQ4UJKwLX42mN+lFQG2QQ08bRDXoYmFJ0Lk8WjSL8/QEo1OmsKkaOG19iIqMikIyhGaII0jNyq9+VLjOzIGPHg20RDh7M55aPpzECe+Hi5nj+fVNiBOH6/FlZcA1peHEYR1bzPYtAlG2so4rzwNvGQDaUpaaOzYhiCAoyclwMgpU/MDtpKmvIVwTzLgTlGjIlRqAucNHjTIN6zyHKNaRhSmG0OxQRZd4ZW3UeeNuQWkPpqaM8icQICqJW/tFVsVpFWhYtxgg8s2IEZSiCtcP2KAIAmcsoS7B4nmhJFiGZcBmbqpCZWRC18p+timeKYBiUmIhOeMfQGEGeQK96z0EDC32btHMXqNS9qFsna7BTgsXzKEU126cqJFFHCXNRBAVRq8XZqtide9y2Fi9uShGYYgjKUAQD29YygJiUsZF1rFXXimkb6vucuGIIVqHcp7KPQGPCK391H/AvcEm1hlSMwBRDMFVDOXy+U9nbt4JVtl5+4yRDkNXH7WceiSJQFJkzXaOOgoViCAriS357YVPVaOKGFEEkWDyNEZhxYchTckGhFNTAtrRUBOrcRS/iWatnRgvXdZ0ic6ZrzDaUiSHQFrXSi+aW+4pgVmuImUFBlpCxMYI8G8rmFIF+KYWjpYogWz8CUQQzisyZrpE0B8tEZmZBVCwgPLE9jzFx/dogA9uCx7P6QgDgGeYaKqYIZuWZ9VQE8auxYc8WRVAAUQTpqaPEhMzMgqiV/8Ce1Z8J+/TiJrxjWvqoXbzExKDnKwLdLgxJsjxrPX1JH52nSIJB11DuSVEEGqMkvypKNnLcUN6vHVuOwA12FpuiCMqoNTTs2Zlz8+sgKXUve7BYFEGYaLq1kIzECFqAyg5Sq+Kx401rg6jGNMD8RdK0GAER5XbrhLNysrpb6iApdS9LsNhxPbgxexG6TDTdWkhGYgQtQJUOmCkCb24nYKwiMCxGAOQvGRDOyhloWHYgKXUvS60c6Ve8SJFNiF1jaggqLFooiqAgyvcbVgTh2iDWVBHMLnCmlZgA8jcjD/fyzZqJUwdJ2/uzlEiQfsWLTNOtXQ/m/AqqISlhoUzEEBRE+X6H/VCw2IkzBIvBYvMUQb4YwcD2azLlNSZVUoYimP6Q+2IIFGFFsKbhseiOKmFOFRaplJlZEHWSZpkz7pxPL657l2egISiiCNR3pGNp4pnrKhosTh/PEEWwyNRlKllDK6kj40wUQUFUG8O+PatRH877VYogLn3UNEOQN0agJvmwp1/XqsRaQxmOd2pM+hIsVqh06zyly7tGHSXMxRAUwPUYrscY2PY0c2bkeHN5v1NDEFr5mFZiAsi+wUoRVgS+312vC0NS6t6wZ2HiMjyPYa0w6HUE+9qGcpOJIlhNHYpAZmYBxhHfr3JtRAOgwPzKx1RFkKv6aNQQaHZhSErdy7IhKjpPBMwlVwjLqWMPiszMAkxX/va8ayMcHIzzhZoYIxj2FnszpyHc/UsZUp1q1C8rMRF+PtVniCKYEk63FpZTRwlzmZkFiK70VEphWBFEW1gC5pWYAMpTBBypy9Q0SW6dLLVykuIMXUYUQXrqiBHIzCxA9CKhXBszA2FPLfl8jMCsEhNAUUXgf0dxGVZNk5S6Nwxlia0iXEZD8OnZ8SXahUXqKFgohqAAo9AFH8C0UX3YZTSIiREom2CSIRj07PyKIORaU4/pQlJDmWmwUxRBbnSsNqsjEizWnMUYga8IVsUIzFUEOdNHQ8F29ZguxDWuB7JVXE1qbtN1dOw/oSN1BIslfbQA0dTCQeAeCbuM1MXe5DLUQP4YwSikCHT0GyetxkQRFEcUQTpkQ5nmRDNKBj0LBybzwWIigGh+5Wha83rAP9Y8MYK5YLGGmSRJq7HphqgsWUNiCObQcSe5jqh2uFUiM7MA0ZWenz7qztXPIaKFAmUmGoJh38IopyKYBou1VQSLP0JRBMXJUrivy0iMQHOiLeSU1I36laMNV0zcUDYMjj3rHoBwRoQKuuu0SkxK3ZsarRT19KUMdTwDDUuK6Iikj2pOdNeparU4duczTYZ9O9Y1ZFSJif5immwaRpPZdzXIkJJZF6oVaZSpGyuFO2za3EY2lM2Rt4dF1xiJItAbtRoMX8hUY5pFRbDYj8AgQZDbrRO+0GZxt9RF9FwqZooghSEIVE+VZYTbiI5lx3UkKXOtTMQQFECtBhcVgRdRBIsxAjuIH5hCnkAvM8+XmMiQklkXSZt54naMJ6H6Wgvz6NiISDemv5GK1aTMzgKEO5EBfiZJkiKIpo+aFB8A8ikCx2Mwzy6qazRUBInpo6rWUJqicwnupa4jweLVTIL+5lWXMJfZWYCoIlBSN7qKjK58XM8zag8BkK88RDTGkiUlsy6SUvfidownfsak+qJhbUTHHtW6Ed20WhViCAoQzQZRO4sPTNy5H360Vr/rAbZBbiEgX3mIhayrDIXc6iJZEWQoQ11DrZg2MsxZlqRL1FXCXGZnAaJF59SPfc/ImbPg0ZWP63mwbbMMQZ6L+OI+DPUZ+qwSV6aPptpH4ErGUAyys3g1dTU1ktlZAHWRUEFfdSHbfcCZs+DRhiuOx8a5hqYNeDJcxKM1eHSsPpqUumdZtLA/ZOlnSFOaBaTW0GpEEbSAqNsgbAgWFMEk7BriaQtLUyhXEehzcVhW8Ctt+mO4wqowQxTBamaKQILF2hJdLSpf9+4Dk7kof1QRuEYrguzBYvW99WwLFumjCJh5aXeotBuiRBHEI4pgNUk9s8tGZmcBoheJsGsjqggW9hEYGiMokjUE6BVAnKbuiSKohIFtw/V4usFSWERtWpUNZRoTdRtEN5HNHp8vMeHHCMz66suIEQCz5j46sCp1L231TL+5jaSPRlG/HREFyURT1KvCrKtRzYwj3auim8jCj88pAmajyksAxdJHo8ZUF0WwKlCXdkNUHSUC2oj67eSoXt4ZRq64hrQnqgiS1MFC+qhrniIo5BpaElhvklWpe+kVgRiCOAZTQyCuoSREEbSAxayh+QBx+PbEZXjBhDexxESejB/12jWRVNs8fQ2qYLUisFMrAikxschQXEMrmZWxkawhbVnoO7DitjqprucZZwiix5iGWSe3cMA93cW1Dlal7qVNfxTXUDwDcQ2tRMXLxDWkMdHsoGR1MF9Dx2WzmtIAocqhGX7Vo5gVt07tC1el7vnpjynTRyVYvID6TsQ1lEy0sGVViCEoQFL6aNJtddEwsehcz7ZgW5SqY5diHJOV4wdgNcsaSkofTbGzmJml1lAC4hpaTV1tTmV2FiBahyZul3H4tjqpjmtejAAI/PsFFYFONepX/QjT7COQxvXJSNbQauL22lSBzM4CRIOAqzKI1EXDYzMNwSBj6ucsRhBVBHpcGVal7kX3h8RRl7RvI7N9BOIaSiLuN1IFMjsLsLChzE5wE0U6b5mYNQQErpKMisAi3600/QyNFMGq1L00Y1WfIYZgEQkWr2bkuLAtmvuNVIHMzgJEYwTR3cTRx5V1N7HWEBC05MyiCNzFIGralMw6WJW6lyaeEe1ZIcyYBYsbHojG1JV6LLOzAAsbyuwEN1Gk85apMQI/eJqhxMRksdZ/1s+oklWpe2niGeozxBAsMlUErriGkqhrM6LMzpyobJDwRcKyCH17vjcBsKgITI0RZF3Nx/XyHfb1iRGs8u+rchjMyReyujYEtRHJGlqNKALNSZL80W5l4cfUStfEonNAdv9+3GpHpxr1aWIEzMuDnXUF+9qIxAhWU9dmRJmdOUlKC1R9CJYpAtfQYHHW1M+47l86BYtXxwjmXX5xxKXICj6SPrqaujYjyuzMSdKuU7XyG8YoglmJCTMNQdr6/Ap/tbMYLHY0qVGfZh9B+HVLP0MUwQKSPrqaaPWCqpDZmZOkjR7q/iAmlVS5Gkw1BHkUQdL3p4N7SKXuJZ2r6I7xpM8AMNexTvBRFzhRBMlEN61WhRiCnMwUQXRFGyiCmFRStUHJMbDEBKCCxdlKTCy41jQyBKsCdWnGKoogGSLCoGdJsHgJEizWnKQ6NDNFEFNraKJqDTEsAw1BnmBxXIzAf675FNJVqXtpejDUVSKgrQxtS4rOLUHSRzUnKUYQrwgWYwRmKoLsMYKk70+HgPFqRbC6K5vUGlrOsG+Ja2gJ0U2rVSGzMydJgcRYRRAp0WxsiYkctYYSYwQaNKcpQxGsKmXddQa2uIaWMYpxn1aBzM6cjBJjBPZCbRDLIj8/vgOKIGutobjvD8jW16AqVq3GhincWEnzRPAZ9m3ZWbyEuE2XVVDZXyCiNUT0IyK6hYhuI6L3x7xmSET/RkR3E9ENRLS5qvGUzTJFEBcYDKdWmhwjyKwI7HjXkDaKYEmQN1P6qCiCWAa2BUfsQCImbCgbATiTmV8E4MUAziai0yKveSuAJ5j5BAAfAfChCsdTKtO0wBgfd9zmIT+1chYsNlMR2HA9hpPyIj5y3IXvarrKnugQLF4cX5g08YxVzW26zrBvQYNTrS1xCRVV0Kvqg9kvwLInuNsP/kVt/zkA/jG4/SUAHyMi4mXFW3Jy5Y/vw0e/tw9rb/5OKZ+3+4ADYPEHPuzZiYpg608fwA2/fNyPEZB5hkB9F//ho9fBSnF8T+6fLHxX6jPefeUtOHiQ7E7Zu6+8c5nErif244WbDk18Xv1AL/zabbjom7+Ifc1je8foLdmL0HUGtoUdT7g468PVnsumyTtfn9o/qWURQRVcc2cfTmQDuAnACQD+hZkviDx/K4CzmXlXcH8ngJcx86OR150P4HwA2Lhx4ylf+MIXMo/l5occfPfXB2D3yrN9hwwIf/78wdyP/M4nXDy418MrN/XnXnvNvRPc8bi/9LEIeO1xfRx9SDV+4z179mDdunWVfPYyHtjjYevOcergn0XA2cf2cdyhs+9h5DA+f8cY+ybL56XrOKWeyyReekQPL31W/N9xPcbnbh9j93j5WI8+xMLrjh/k+vtNncu6uOFBB9fvKvd3qSN552vcbyQvZ5xxxk3MfGrsk8xc+T8AGwBsA/CCyOO3AtgUur8TwGHLPuuUU07hvGzbti33e9tEF46zC8fI3I3jlGOsBwA3csJ1tRbHJTM/GRiCsyNP3Q/gKAAgoh6AQwE8VseYBEEQBJ8qs4YOJ6INwe2DAJwF4I7Iy7YCeFNw+1wA3w4slyAIglATVTrmjgBweRAnsABcycxXEdH/gC9RtgK4DMC/EtHdAB4H8GcVjkcQBEGIocqsoR0ATop5/H2h2wcA/GlVYxAEQRBWI8nNgiAIHUcMgSAIQscRQyAIgtBxxBAIgiB0nEp3FlcBET0C4N6cbz8MwKMrX9V+unCcXThGoBvHKcdYD8cw8+FxT7TOEBSBiG7kpC3WBtGF4+zCMQLdOE45xuYR15AgCELHEUMgCILQcbpmCC5tegA10YXj7MIxAt04TjnGhulUjEAQBEFYpGuKQBAEQYgghkAQBKHjdMYQENHZRPQLIrqbiN7T9HjKgIiOIqJtRPRzIrqNiN4RPP50IrqaiO4K/n9a02MtChHZRPQTIroquH8sEd0QnM9/I6J8LcA0gog2ENGXiOgOIrqdiH7ftHNJRO8K5uqtRHQFEa0x4VwS0aeI6OGg66J6LPbckc/FwfHuIKKTmxu5TycMQVAK+18AvBrAiQDOI6ITmx1VKTgA/oaZTwRwGoC3Bcf1HgDXMvNzAFwb3G877wBwe+j+hwB8hJlPAPAEgLc2Mqpy+WcA32Dm5wF4EfzjNeZcEtGRAP4awKnM/AIANvzS8yacy89gsfFW0rl7NYDnBP/OB3BJTWNMpBOGAMBLAdzNzL9k5jGALwA4p+ExFYaZH2Tmm4Pbu+FfOI6Ef2yXBy+7HMCfNDLAkiCiTQD+GMAng/sE4EwAXwpeYsIxHgrgFfB7dICZx0FnP6POJfzS9wcFHQkPBvAgDDiXzHwd/J4qYZLO3TkAPht0kLwewAYiOqKWgSbQFUNwJID7Qvd3BY8ZAxFtht//4QYAG5n5weCp3wDY2NS4SuKjAP4OgBfcfwaAJ5nZCe6bcD6PBfAIgE8HLrBPEtFaGHQumfl+ABcB+DV8A/AUgJtg3rlUJJ077a5HXTEERkNE6wB8GcA7mfm34eeC1p+tzREmotcCeJiZb2p6LBXTA3AygEuY+SQAexFxAxlwLp8GfzV8LIBnA1iLRXeKkeh+7rpiCO4HcFTo/qbgsdZDRH34RuDzzPyV4OGHlNQM/n+4qfGVwMsBvI6IfgXfpXcmfF/6hsC9AJhxPncB2MXMNwT3vwTfMJh0Lv8QwD3M/AgzTwB8Bf75Ne1cKpLOnXbXo64Ygh8DeE6QnTCAH6Da2vCYChP4yi8DcDszfzj01FYAbwpuvwnA1+oeW1kw83uZeRMzb4Z/3r7NzH8OYBuAc4OXtfoYAYCZfwPgPiJ6bvDQqwD8HAadS/guodOI6OBg7qpjNOpchkg6d1sBvDHIHjoNwFMhF1IzMHMn/gF4DYA7AewE8N+bHk9Jx3Q6fLm5A8BPg3+vge9DvxbAXQCuAfD0psda0vFuAXBVcPs4AD8CcDeALwIYNj2+Eo7vxQBuDM7nVwE8zbRzCeD9AO4AcCuAfwUwNOFcArgCftxjAl/dvTXp3AEg+FmMOwH8DH4WVaPjlxITgiAIHacrriFBEAQhATEEgiAIHUcMgSAIQscRQyAIgtBxxBAIgiB0HDEEQuchIiaiz4Xu94jokVCl09cVqVhLRO8kooPLGKsgVIEYAkHwyzm8gIgOCu6fhdBOT2beyswfLPD574RfYE0QtEQMgSD4fB1+hVMAOA/+BiEAABG9mYg+Ftz+TFBL/gdE9EsiOjd4fItSEMH9jwXv+2v4dXW2EdG24Lk/IqIfEtHNRPTFoFYUiOiDQW+JHUR0US1HLQgQQyAIii8A+DMiWgPghfCruCZxBPxd3a8FsFQpMPPFAB4AcAYzn0FEhwH4BwB/yMwnw99J/G4iegaA1wP4XWZ+IYAPFD0gQUhLb/VLBMF8mHlHUMr7PPjqYBlfZWYPwM+JKGtZ6NPgN0f6vl9uBwMAP4RfkvkAgMsCZXFV4icIQsmIIRCEGVvh18vfAr9OTBKj0G0K/ncwr7DXJLyXAFzNzOctPEH0UviF2M4F8F/hV1oVhMoR15AgzPgUgPcz889yvPdeACcS0ZCINsC/oCt2A1gf3L4ewMuJ6AQAIKK1RPQ7QZzgUGb+OoB3wW9VKQi1IIpAEAKYeReAi3O+9z4iuhJ+Vc17APwk9PSlAL5BRA8EcYI3A7iCiIbB8/8A31h8LYhREIB35zwMQciMVB8VBEHoOOIaEgRB6DhiCARBEDqOGAJBEISOI4ZAEASh44ghEARB6DhiCARBEDqOGAJBEISO8/8Bc62b9JSlvacAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "replace_num ={'qt':'1.0','qh':'1.0','tr':'2.0','al':'3.0','ah':'3.0','aw':'4.0'}\n",
    "datafou=[replace_num[i] if i in replace_num else i for i in data]\n",
    "plt.plot(datafou)\n",
    "plt.grid()\n",
    "plt.xlabel('Minutes')\n",
    "plt.ylabel('Sleep State')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c0d134c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sleep State')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datafou=np.array(datafou,dtype=np.float32)\n",
    "plt.plot(datafou)\n",
    "plt.grid()\n",
    "plt.xlabel('Minutes')\n",
    "plt.ylabel('Sleep State')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36972382",
   "metadata": {},
   "source": [
    "上面两幅图y轴坐标 从上到下相反，关键原因是，数据类型不统一"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70d549a9",
   "metadata": {},
   "source": [
    "使用睡眠状态和值 1 到 3 之间的这种分配规则，数据的循环行为更加清晰。使用新分配重复光谱分析。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a2a5e9a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Periodogram of Sleep States')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "[F,Pxx] = signal.periodogram(signal.detrend(datafou),Fs,nfft=256)\n",
    "plt.plot(F,Pxx)\n",
    "plt.grid()\n",
    "plt.xlabel('Cycles/Hour')\n",
    "plt.title('Periodogram of Sleep States')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a66f8b4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.171875\n"
     ]
    }
   ],
   "source": [
    "Pxx=Pxx.tolist()\n",
    "print(F[Pxx.index(max(Pxx))])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
